CN116700186A - Electrical control cabinet processing quality detection system based on big data detection - Google Patents
Electrical control cabinet processing quality detection system based on big data detection Download PDFInfo
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- 238000012545 processing Methods 0.000 title claims abstract description 78
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- 238000011156 evaluation Methods 0.000 claims abstract description 92
- 238000004458 analytical method Methods 0.000 claims abstract description 64
- 238000012216 screening Methods 0.000 claims abstract description 25
- 238000007405 data analysis Methods 0.000 claims abstract description 7
- 238000004381 surface treatment Methods 0.000 claims description 54
- 230000002159 abnormal effect Effects 0.000 claims description 23
- 238000000034 method Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 9
- 238000009713 electroplating Methods 0.000 claims description 7
- 239000011248 coating agent Substances 0.000 claims description 5
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- 238000005507 spraying Methods 0.000 claims description 4
- 230000005856 abnormality Effects 0.000 claims description 2
- 238000003754 machining Methods 0.000 description 12
- 238000012423 maintenance Methods 0.000 description 4
- 230000001681 protective effect Effects 0.000 description 4
- 206010014357 Electric shock Diseases 0.000 description 3
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32368—Quality control
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
The application discloses a big data detection-based electrical control cabinet processing quality detection system, which comprises a data acquisition module, an analysis module, a mark screening module, a comprehensive analysis module and an early warning module; the data acquisition module is used for acquiring a plurality of data including cabinet body data and electric supporting facility data during processing of the electric control cabinet, and transmitting the cabinet body data and the electric supporting facility data to the analysis module after acquisition; the analysis module is used for establishing a data analysis model after acquiring the cabinet data and the electric supporting facility data, generating an evaluation index and transmitting the evaluation index to the mark screening module. According to the application, the evaluation index is generated by collecting a plurality of data during processing of the electrical control cabinet, the quality of the processed electrical control cabinet is evaluated in time by the evaluation index, and the electrical control cabinet with unqualified processing quality of the electrical control cabinet is screened out, so that the processing quality of the electrical control cabinet is detected.
Description
Technical Field
The application relates to the technical field of electrical control cabinet processing, in particular to an electrical control cabinet processing quality detection system based on big data detection.
Background
The electrical control cabinet is characterized in that the switch equipment, the measuring instrument, the protection electrical appliance and the auxiliary equipment are assembled in or on the closed or semi-closed metal cabinet according to the electrical wiring requirement, the arrangement of the electrical control cabinet meets the requirement of normal operation of an electric power system, the electrical control cabinet is convenient to overhaul, and the safety of personnel and surrounding equipment is not endangered. The circuit can be switched on or off by means of a manual or automatic switch during normal operation. In case of failure or abnormal operation, the circuit is cut off or an alarm is given by means of a protective electric appliance. The measuring instrument can display various parameters in operation, and can also adjust certain electrical parameters to prompt or signal deviation from a normal working state. The method is commonly used in various power generating, distributing and transforming substations.
The prior art has the following defects: when the electrical control cabinet is processed, the quality of the processing needs to be detected, the prior art mostly carries out uniform quality detection on the electrical control cabinets in batches after the electrical control cabinet is processed in batches, and unqualified electrical control cabinets are screened out for maintenance, however, when the processing quality of the electrical control cabinets has the quality problem in batches, the unqualified batch condition of the electrical control cabinets which is easy to process cannot be found in time, and thus the processing efficiency of the electrical control cabinets can be greatly reduced.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The application aims to provide a processing quality detection system of an electrical control cabinet based on big data detection, which generates an evaluation index by collecting a plurality of data during processing of the electrical control cabinet, timely evaluates the processed quality of the electrical control cabinet through the evaluation index, screens out the electrical control cabinet with unqualified processing quality of the electrical control cabinet, and thereby realizes detection of the processing quality of the electrical control cabinet, so as to solve the problems in the background art.
In order to achieve the above object, the present application provides the following technical solutions: the electrical control cabinet processing quality detection system based on big data detection comprises a data acquisition module, an analysis module, a mark screening module, a comprehensive analysis module and an early warning module;
the data acquisition module is used for acquiring a plurality of data including cabinet body data and electric supporting facility data during processing of the electric control cabinet, and transmitting the cabinet body data and the electric supporting facility data to the analysis module after acquisition;
the analysis module is used for establishing a data analysis model after acquiring cabinet data and electric supporting facility data, generating an evaluation index and transmitting the evaluation index to the mark screening module;
the mark screening module is used for comparing an evaluation index generated by the quality detection of the electrical control cabinet with an evaluation index reference threshold value, screening out unqualified electrical control cabinets and transmitting information of unqualified processing quality of the electrical control cabinets to the comprehensive analysis module;
the comprehensive analysis module is used for acquiring evaluation indexes generated by quality detection of a plurality of subsequent electrical control cabinets after receiving information of unqualified processing quality of the electrical control cabinets, establishing an analysis set, comprehensively analyzing the evaluation indexes in the analysis set, transmitting an analysis result to the early warning module, and sending or not sending an early warning prompt through the early warning module.
Preferably, the cabinet body data comprises cabinet body surface processing uniformity, the data acquisition module is used for calibrating the cabinet body surface processing uniformity to JYDx after acquisition, the electric supporting facility data comprises transformer power factor and breaker overload deviation, and the data acquisition module is used for calibrating the transformer power factor and the breaker overload deviation to BGLx and GZPx respectively after acquisition.
Preferably, the step of obtaining the uniformity of the cabinet surface treatment of the electrical control cabinet is as follows:
s1, dividing the surface of a cabinet body after treatment into N areas, obtaining the surface treatment thickness of the N areas, calibrating the surface treatment thickness to be Hi, wherein i represents the number of the surface treatment thickness, i=1, 2, 3, 4, … … and N, wherein N is a positive integer;
when the surface treatment of the cabinet body is spraying, the thickness of the surface treatment of the cabinet body can be measured by adopting a coating thickness gauge, and when the surface treatment of the cabinet body is electroplating, the thickness of the surface treatment of the cabinet body can be measured by adopting an electroplating thickness gauge;
s2, calculating standard deviation of surface treatment thickness of N areas on the surface of the cabinet body, calibrating the standard deviation as S1, and adopting a calculation formula:
，
wherein ,the average value of the surface treatment thickness of N areas of the surface of the cabinet body;
s3, acquiring the surface treatment uniformity JYDx of the cabinet body through the standard deviation S1 of the surface treatment thickness of the N areas of the cabinet body.
Preferably, the specific steps for obtaining the power factor of the transformer are as follows:
s1, determining active power and reactive power of a transformer;
s2, calculating the apparent power of the transformer, calibrating the apparent power as S, and calculating the expression as follows:wherein P represents active power, and Q represents reactive power;
s3, calculating a power factor, calibrating the power factor as PF, and calculating the expression: pf=p/S;
and S4, obtaining a transformer power factor BGLx through a value of the power factor PF.
Preferably, the step of acquiring overload deviation of the circuit breaker is as follows:
s1, determining rated current, and calibrating the rated current as Im;
s2, using a measuring instrument to clamp a current measuring clamp on a circuit protected by the circuit breaker, measuring an actual current value, and calibrating the actual current value to be Ix;
s3, calculating overload deviation, calibrating the overload deviation as Px, and according to the calculation formula: px= [ (Im-Ix)/Im ]. Times.100%;
s4, acquiring overload deviation GZPx of the circuit breaker through the value of the overload deviation Px.
Preferably, after the analysis module obtains the cabinet surface processing uniformity JYDx, the transformer power factor BGLx and the overload deviation GZPx of the circuit breaker, a data analysis model is established to generate an evaluation index PYg according to the following formula:
，
wherein, tau, alpha and beta are preset proportionality coefficients of the cabinet surface treatment uniformity JYDx, the transformer power factor BGLx and the overload deviation GZPx of the circuit breaker respectively, and tau, alpha and beta are all larger than 0.
Preferably, after the mark screening module obtains the evaluation index generated by the quality detection of the electrical control cabinet, the evaluation index is compared with the evaluation index reference threshold, if the evaluation index is greater than or equal to the evaluation index reference threshold, the electrical control cabinet is marked as an unqualified product and screened out through the mark screening module, the information that the processing quality of the electrical control cabinet is unqualified is transmitted to the comprehensive analysis module, and if the evaluation index is smaller than the evaluation index reference threshold, the electrical control cabinet is not marked through the mark screening module.
Preferably, after receiving information that the machining quality of the electrical control cabinet is unqualified, the comprehensive analysis module acquires evaluation indexes generated by quality detection of a plurality of subsequent electrical control cabinets to establish an analysis set, and the analysis set is marked as W, then W= { PYg } = { PY1, PY2, … and PYk }, and the average value and standard deviation of a plurality of evaluation indexes PYg are calculated;
the calculation formula of the average value of the evaluation index PYg in the analysis set is as follows:
the calculation formula of the standard deviation of the evaluation index PYg in the analysis set is as follows:
；
wherein g=1, 2, 3, 4, … …, k is a positive integer,for the average of the evaluation index PYg in the analysis set, s2 is the standard deviation of the evaluation index PYg in the analysis set.
Preferably, if the average value of the evaluation index PYg is smaller than the evaluation index reference threshold and the standard deviation is smaller than the standard deviation reference threshold, generating accidental abnormal information through the comprehensive analysis module and transmitting the accidental abnormal information to the early warning module, and not sending an early warning prompt through the early warning module;
if the average value of the evaluation index PYg is greater than or equal to the evaluation index reference threshold value, the average value of the evaluation index PYg is greater than or equal to the evaluation index reference threshold value and the standard deviation is smaller than the standard deviation reference threshold value, generating abnormal information through the comprehensive analysis module, transmitting the abnormal information to the early warning module, and sending an early warning prompt through the early warning module to prompt the processing personnel that the processing quality of the electrical control cabinet is not in the condition of accidental abnormality.
In the technical scheme, the application has the technical effects and advantages that:
according to the application, the evaluation index is generated by collecting a plurality of data during processing of the electrical control cabinet, the quality of the processed electrical control cabinet is evaluated in time by the evaluation index, and the electrical control cabinet with unqualified processing quality of the electrical control cabinet is screened out, so that the processing quality of the electrical control cabinet is detected;
according to the application, when the electrical control cabinet with unqualified processing quality is found, the evaluation indexes generated by the quality detection of a plurality of subsequent electrical control cabinets are comprehensively analyzed, if the processing quality of the electrical control cabinet is not abnormal accidentally, an early warning prompt is sent to prompt a processor to stop the processing production line in time, the abnormal electrical control cabinet is timely checked, the problem that the processing of the electrical control cabinet is abnormal is timely found, and the condition that the processed electrical control cabinets are unqualified in batches is effectively avoided, so that the processing efficiency of the electrical control cabinet is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
Fig. 1 is a schematic diagram of a module of the processing quality detection system of an electrical control cabinet based on big data detection.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The application provides a big data detection-based electrical control cabinet processing quality detection system as shown in fig. 1, which comprises a data acquisition module, an analysis module, a marker screening module, a comprehensive analysis module and an early warning module;
the data acquisition module is used for acquiring a plurality of data including cabinet body data and electric supporting facility data during processing of the electric control cabinet, and transmitting the cabinet body data and the electric supporting facility data to the analysis module after acquisition;
the cabinet body data comprise cabinet body surface processing uniformity, and after the cabinet body surface processing uniformity is acquired, the data acquisition module marks the cabinet body surface processing uniformity as JYDx;
after the processing of the electrical control cabinet body is finished, the surface of the cabinet body needs to be treated, including spraying, galvanizing, electroplating and the like, so that the corrosion resistance, the insulativity, the wear resistance and the like of the electrical control cabinet are improved;
the non-uniform surface treatment of the control cabinet shell may have the following effects on the electrical control cabinet:
the appearance quality is reduced: uneven surface treatment can cause problems such as uneven appearance, chromatic aberration or speckles of the control cabinet, and the appearance quality is reduced, which can affect the image and the aesthetic degree of the electrical control cabinet in front of clients,
reducing the protective performance: uneven surface treatment of the shell may affect the protective performance, for example, if the surface coating is defective or unevenly coated, the protective effect of the control cabinet on environmental conditions (such as humidity, corrosion, chemicals, etc.) may be reduced, and thus problems such as corrosion, oxidation or breakage may be caused;
impact durability: uneven surface treatment may lead to reduced durability of the housing, and if the surface coating is uneven or defective, it may be easily peeled off, worn or damaged during use, reducing the service life of the control cabinet;
impact maintenance and cleaning: uneven surface treatment may cause more difficult cleaning and maintenance processes, and a control cabinet with uneven surface treatment is more prone to accumulate dirt, dust or difficult to clean, increasing the difficulty of maintenance and cleaning;
potential safety hazard: uneven surface treatment may cause electric shock or other potential safety hazards, and if the surface treatment of the control cabinet shell is defective or uneven, the surface treatment may cause exposure or insufficient insulation of electrical components, thereby increasing electric shock risk;
therefore, when the processing quality of the electrical control cabinet is detected, the surface treatment condition of the electrical control cabinet needs to be detected;
the method for obtaining the surface treatment uniformity of the electrical control cabinet comprises the following steps:
s1, dividing the surface of a cabinet body after treatment into N areas, obtaining the surface treatment thickness of the N areas, calibrating the surface treatment thickness to be Hi, wherein i represents the number of the surface treatment thickness, i=1, 2, 3, 4, … … and N, wherein N is a positive integer;
when the surface treatment of the cabinet body is spraying, the thickness of the surface treatment of the cabinet body can be measured by adopting a coating thickness gauge, and when the surface treatment of the cabinet body is electroplating, the thickness of the surface treatment of the cabinet body can be measured by adopting an electroplating thickness gauge;
s2, calculating standard deviation of surface treatment thickness of N areas on the surface of the cabinet body, calibrating the standard deviation as S1, and adopting a calculation formula:
，
wherein ,the average value of the surface treatment thickness of N areas of the surface of the cabinet body;
s3, acquiring the surface treatment uniformity JYDx of the cabinet body through the standard deviation S1 of the surface treatment thickness of the N areas of the cabinet body;
the higher the uniformity of the cabinet surface treatment, the worse the uniformity of the thickness of the cabinet surface treatment, and the lower the uniformity of the cabinet surface treatment, the better the uniformity of the thickness of the cabinet surface treatment;
the data acquisition module respectively marks the power factor of the transformer and the overload deviation of the circuit breaker as BGLx and GZPx after acquisition;
transformers have important roles in voltage conversion, insulation, current conversion, short-circuit protection, noise reduction, etc. in electrical control cabinets, they help ensure that electrical equipment within the control cabinet operates at the proper voltage and current, and provide electrical safety and reliability;
the influence of the power factor of the transformer on the electrical control cabinet is mainly expressed in the following aspects:
consumption of electric energy: the power factor refers to the ratio between the actual power and the apparent power, the numerical range is between 0 and 1, and the lower the power factor is, the higher the reactive power required by the transformer is, so that the consumption of electric energy is increased, and the energy consumption and the electric cost for the operation of the electric control cabinet are increased;
grid load: the low power factor can increase the reactive load of the power grid, the reactive power does not generate efficacy in power transmission, the transmission capacity of the power grid is occupied, when the power factor of a transformer in an electrical control cabinet is low, the reactive power load of the power grid can be increased, and the stability and the operation efficiency of a power system are affected;
voltage stability: the low power factor can cause unstable voltage, and the flowing of reactive power can cause voltage fluctuation and voltage reduction, so that the normal operation of each device in the electrical control cabinet is affected, and especially for devices with higher voltage requirements, the low power factor can cause unstable operation and even damage of the devices;
transformer load capacity: the magnitude of the power factor also affects the load capacity of the transformer, the lower the power factor, the higher the apparent power required by the transformer for the same active power demand, and therefore its load capacity can be reduced, which can lead to overload of the transformer or failure to meet the power demand of the device;
therefore, when the processing quality of the electrical control cabinet is detected, the power factor of the transformer needs to be detected;
the specific steps of the power factor acquisition of the transformer are as follows:
s1, determining active power and reactive power of a transformer;
the power meter or the multifunctional electric energy meter and other instrument equipment are used, are connected to the input side or the output side of the transformer, can directly measure the active power and the reactive power of the transformer, can generally measure parameters such as current, voltage, power factor and the like, and calculate the values of the active power and the reactive power according to the measurement results;
s2, calculating the apparent power of the transformer, calibrating the apparent power as S, and calculating the expression as follows:wherein P (in watts) represents active power and Q (in spent) represents reactive power;
s3, calculating a power factor, calibrating the power factor as PF, and calculating the expression: pf=p/S;
s4, obtaining a transformer power factor BGLx through a value of the power factor PF;
the power factor of the transformer (the value range of the power factor is between-1 and 1) can be in the following cases:
positive power factor: when the active power and the apparent power of the transformer are in the same phase, the power factor is positive, which means that the active power provided by the transformer is basically consistent with the required apparent power, the utilization efficiency is higher, the range of the positive power factor is from 0 to 1, and the closer to 1, the higher the power factor is, the better the power utilization rate is;
zero power factor: when the active power of the transformer is zero but a certain reactive power exists, the power factor is zero, which means that the power provided by the transformer is mainly reactive power and no actual useful power is output;
negative power factor: when the active power of the transformer is in antiphase with the apparent power, the power factor is negative, which means that the active power provided by the transformer is opposite to the direction of the apparent power required by the transformer, and the transformer mainly absorbs reactive power without generating useful power, and the negative power factor may also mean that the power system has a problem or abnormal operation state, for example, when the transformer is connected with a capacitive load (such as a capacitor), the reactive power generated by the capacitive load exceeds the active power provided by the transformer, so that the power factor is negative, which may be caused by overcompensation of the capacitive load or other power system problems;
the circuit breaker plays a role in protecting electrical equipment and circuits in the electrical control cabinet, ensures safe and reliable operation of the circuit breaker, and provides convenience for control and operation;
the overload deviation of the circuit breaker is larger and can have the following effects on the electrical control cabinet:
damage to electrical equipment: a large overload deviation means that the rated current allowed by the circuit breaker is exceeded to a large extent, which may lead to long operation of the electrical equipment in overload situations, thereby causing the risk of overheating, damage or even fire of the equipment;
overload of the electrical control cabinet: the circuit breaker with larger overload deviation can not trip in time, so that the electrical equipment continuously operates in an overload state, and the whole electrical control cabinet is overloaded, which can cause the temperature in the control cabinet to rise, and the normal operation and the service life of the equipment are affected;
the stability of the power supply system is reduced: the overload deviation of the circuit breaker can cause that the load of the power supply system exceeds the design capacity, so that the operation of the power supply system is unstable, which can cause voltage drop, power fluctuation and other power quality problems, thereby influencing the normal operation of other equipment in the electrical control cabinet;
potential safety hazard: the circuit breaker with larger overload deviation can not trip in time, so that the electrical equipment continuously works in an overload state, the fault risk of the electrical system and the equipment is increased, and the potential safety hazards such as fire, electric shock, short circuit and the like can be caused, and the danger is caused to personnel and the equipment;
therefore, when the processing quality of the electrical control cabinet is detected, the overload condition of the circuit breaker needs to be detected;
the overload deviation of the circuit breaker is obtained by the following steps:
s1, determining rated current, and calibrating the rated current as Im;
looking at the rated current of the circuit breaker, which is usually found in the nameplate or technical specification of the equipment, wherein the rated current refers to the maximum current value of the circuit breaker capable of safely operating;
s2, using a measuring instrument to clamp a current measuring clamp on a circuit protected by the circuit breaker, measuring an actual current value, and calibrating the actual current value to be Ix;
the instrument may be an ammeter, or the like, and is not particularly limited herein, and it is necessary to ensure accuracy and a proper range of the measuring instrument.
S3, calculating overload deviation, calibrating the overload deviation as Px, and according to the calculation formula: px= [ (Im-Ix)/Im ]. Times.100%;
s4, acquiring overload deviation GZPx of the circuit breaker through the value of the overload deviation Px;
the analysis module is used for establishing a data analysis model after acquiring cabinet data and electric supporting facility data, generating an evaluation index and transmitting the evaluation index to the mark screening module;
after the analysis module obtains the cabinet surface processing uniformity JYDx, the transformer power factor BGLx and the overload deviation GZPx of the circuit breaker, a data analysis model is established, an evaluation index PYg is generated, and the following formula is adopted:
，
wherein, tau, alpha and beta are respectively preset proportionality coefficients of the cabinet surface treatment uniformity JYDx, the transformer power factor BGLx and the overload deviation GZPx of the circuit breaker, and tau, alpha and beta are all larger than 0;
the formula shows that the larger the surface treatment uniformity of the cabinet body is, the smaller the power factor of the transformer is, the larger the overload deviation of the circuit breaker is, namely, the larger the appearance value of the evaluation index PYg is, the lower the surface treatment uniformity of the cabinet body is, the larger the power factor of the transformer is, the smaller the overload deviation of the circuit breaker is, namely, the smaller the appearance value of the evaluation index PYg is, the better the processing quality of the electrical control cabinet is;
the mark screening module is used for comparing an evaluation index generated by the quality detection of the electrical control cabinet with an evaluation index reference threshold value, screening out unqualified electrical control cabinets and transmitting information of unqualified processing quality of the electrical control cabinets to the comprehensive analysis module;
after an evaluation index generated by quality detection of the electrical control cabinet is obtained by the mark screening module, the evaluation index is compared with an evaluation index reference threshold, if the evaluation index is larger than or equal to the evaluation index reference threshold, the quality of the electrical control cabinet is indicated to be unqualified, the electrical control cabinet is marked as an unqualified product and screened out by the mark screening module, the information of the unqualified processing quality of the electrical control cabinet is transmitted to the comprehensive analysis module, if the evaluation index is smaller than the evaluation index reference threshold, the quality of the electrical control cabinet is indicated to be qualified, and the electrical control cabinet is not marked by the mark screening module, so that the electrical control cabinet is indicated to be qualified;
according to the application, the evaluation index is generated by collecting a plurality of data during processing of the electrical control cabinet, the quality of the processed electrical control cabinet is evaluated in time by the evaluation index, and the electrical control cabinet with unqualified processing quality of the electrical control cabinet is screened out, so that the processing quality of the electrical control cabinet is detected;
the comprehensive analysis module is used for acquiring evaluation indexes generated by quality detection of a plurality of subsequent electrical control cabinets after receiving information of unqualified processing quality of the electrical control cabinets, establishing an analysis set, comprehensively analyzing the evaluation indexes in the analysis set, transmitting an analysis result to the early warning module, and sending or not sending an early warning prompt through the early warning module;
after receiving information of unqualified processing quality of the electrical control cabinet, the comprehensive analysis module acquires evaluation indexes generated by quality detection of a plurality of subsequent electrical control cabinets to establish an analysis set, and the analysis set is marked as W, so that W= { PYg + = { PY1, PY2, … and PYk }, and the average value and standard deviation of a plurality of evaluation indexes PYg are calculated;
if the average value of the evaluation index PYg is smaller than the evaluation index reference threshold and the standard deviation is smaller than the standard deviation reference threshold, the condition that the machining quality of the electrical control cabinet is accidentally abnormal is indicated, accidental abnormal information is generated through the comprehensive analysis module and transmitted to the early warning module, the early warning module does not send out early warning prompt, the electrical control cabinet with unqualified machining quality is screened out through the mark screening module, and the electrical control cabinet is overhauled later;
if the average value of the evaluation index PYg is larger than or equal to the evaluation index reference threshold value, the average value of the evaluation index PYg is larger than or equal to the evaluation index reference threshold value and the standard deviation is smaller than the standard deviation reference threshold value, the condition that the machining quality of the electrical control cabinet is not accidentally abnormal is indicated, abnormal information is generated through the comprehensive analysis module and transmitted to the early warning module, the early warning module sends out early warning prompt to prompt the machining quality of the machining personnel that the machining quality of the electrical control cabinet is not accidentally abnormal, when the early warning prompt is received, the machining personnel stops the machining production line in time, checks the abnormal electrical control cabinet in time, finds out that the machining of the electrical control cabinet is abnormal, effectively avoids the condition that the machined electrical control cabinet is unqualified in batches, so that the machining efficiency of the electrical control cabinet is greatly improved, meanwhile, the electrical control cabinet with unqualified machining quality is screened out through the mark screening module, and is overhauled later;
the calculation formula of the average value of the evaluation index PYg in the analysis set is as follows:
the calculation formula of the standard deviation of the evaluation index PYg in the analysis set is as follows:
；
wherein g=1, 2, 3, 4, … …, k is a positive integer,for the average value of the evaluation index PYg in the analysis set, s2 is the standard deviation of the evaluation index PYg in the analysis set;
according to the application, when the electrical control cabinet with unqualified processing quality is found, the evaluation indexes generated by the quality detection of a plurality of subsequent electrical control cabinets are comprehensively analyzed, if the processing quality of the electrical control cabinet is not abnormal accidentally, an early warning prompt is sent to prompt a processor to stop the processing production line in time, the abnormal electrical control cabinet is timely checked, the problem that the processing of the electrical control cabinet is abnormal is timely found, and the condition that the processed electrical control cabinets are unqualified in batches is effectively avoided, so that the processing efficiency of the electrical control cabinet is greatly improved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
While certain exemplary embodiments of the present application have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the application. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the application, which is defined by the appended claims.
It is noted that relational terms such as first and second, and the like, if any, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The electrical control cabinet processing quality detection system based on big data detection is characterized by comprising a data acquisition module, an analysis module, a mark screening module, a comprehensive analysis module and an early warning module;
the data acquisition module is used for acquiring a plurality of data including cabinet body data and electric supporting facility data during processing of the electric control cabinet, and transmitting the cabinet body data and the electric supporting facility data to the analysis module after acquisition;
the analysis module is used for establishing a data analysis model after acquiring cabinet data and electric supporting facility data, generating an evaluation index and transmitting the evaluation index to the mark screening module;
the mark screening module is used for comparing an evaluation index generated by the quality detection of the electrical control cabinet with an evaluation index reference threshold value, screening out unqualified electrical control cabinets and transmitting information of unqualified processing quality of the electrical control cabinets to the comprehensive analysis module;
the comprehensive analysis module is used for acquiring evaluation indexes generated by quality detection of a plurality of subsequent electrical control cabinets after receiving information of unqualified processing quality of the electrical control cabinets, establishing an analysis set, comprehensively analyzing the evaluation indexes in the analysis set, transmitting an analysis result to the early warning module, and sending or not sending an early warning prompt through the early warning module.
2. The electrical control cabinet processing quality detection system based on big data detection of claim 1, wherein the cabinet body data comprises cabinet surface processing uniformity, the data acquisition module marks the cabinet surface processing uniformity as JYDx after acquisition, the electrical supporting facility data comprises transformer power factor and breaker overload deviation, and the data acquisition module marks the transformer power factor and the breaker overload deviation as BGLx and GZPx after acquisition.
3. The electrical control cabinet processing quality detection system based on big data detection according to claim 2, wherein the step of obtaining the cabinet surface processing uniformity of the electrical control cabinet is as follows:
s1, dividing the surface of a cabinet body after treatment into N areas, obtaining the surface treatment thickness of the N areas, calibrating the surface treatment thickness to be Hi, wherein i represents the number of the surface treatment thickness, i=1, 2, 3, 4, … … and N, wherein N is a positive integer;
when the surface treatment of the cabinet body is spraying, the thickness of the surface treatment of the cabinet body can be measured by adopting a coating thickness gauge, and when the surface treatment of the cabinet body is electroplating, the thickness of the surface treatment of the cabinet body can be measured by adopting an electroplating thickness gauge;
s2, calculating standard deviation of surface treatment thickness of N areas on the surface of the cabinet body, calibrating the standard deviation as S1, and adopting a calculation formula:
，
wherein ,the average value of the surface treatment thickness of N areas of the surface of the cabinet body;
s3, acquiring the surface treatment uniformity JYDx of the cabinet body through the standard deviation S1 of the surface treatment thickness of the N areas of the cabinet body.
4. The electrical control cabinet processing quality detection system based on big data detection according to claim 3, wherein the specific steps of transformer power factor acquisition are as follows:
s1, determining active power and reactive power of a transformer;
s2, calculating the apparent power of the transformer, calibrating the apparent power as S, and calculating the expression as follows:wherein P represents active power, and Q represents reactive power;
s3, calculating a power factor, calibrating the power factor as PF, and calculating the expression: pf=p/S;
and S4, obtaining a transformer power factor BHLx through a value of the power factor PF.
5. The electrical control cabinet process quality detection system based on big data detection of claim 4, wherein the step of acquiring overload deviation of the circuit breaker is as follows:
s1, determining rated current, and calibrating the rated current as Im;
s2, using a measuring instrument to clamp a current measuring clamp on a circuit protected by the circuit breaker, measuring an actual current value, and calibrating the actual current value to be Ix;
s3, calculating overload deviation, calibrating the overload deviation as Px, and according to the calculation formula: px= [ (Im-Ix)/Im ]. Times.100%;
s4, acquiring overload deviation GZPx of the circuit breaker through the value of the overload deviation Px.
6. The electrical control cabinet processing quality detection system based on big data detection according to claim 5, wherein after the analysis module obtains the cabinet surface processing uniformity JYDx, the transformer power factor BGLx and the overload deviation GZPx of the circuit breaker, a data analysis model is built to generate an evaluation index PYg according to the following formula:
，
wherein, tau, alpha and beta are preset proportionality coefficients of the cabinet surface treatment uniformity JYDx, the transformer power factor BGLx and the overload deviation GZPx of the circuit breaker respectively, and tau, alpha and beta are all larger than 0.
7. The system for detecting the processing quality of the electrical control cabinet based on big data detection according to claim 6, wherein after the marking and screening module obtains the evaluation index generated by the quality detection of the electrical control cabinet, the evaluation index is compared with the evaluation index reference threshold, if the evaluation index is greater than or equal to the evaluation index reference threshold, the electrical control cabinet is marked as an unqualified product and screened out through the marking and screening module, and the information that the processing quality of the electrical control cabinet is unqualified is transmitted to the comprehensive analysis module, and if the evaluation index is smaller than the evaluation index reference threshold, the electrical control cabinet is not marked through the marking and screening module.
8. The system for detecting the processing quality of the electrical control cabinet based on big data detection according to claim 7, wherein after receiving the information that the processing quality of the electrical control cabinet is unqualified, the comprehensive analysis module acquires evaluation indexes generated by quality detection of a plurality of subsequent electrical control cabinets to establish an analysis set, and the analysis set is marked as W, then W= { PYg } = { PY1, PY2, …, PYk }, and calculates the average value and standard deviation of a plurality of evaluation indexes PYg;
the calculation formula of the average value of the evaluation index PYg in the analysis set is as follows:
the calculation formula of the standard deviation of the evaluation index PYg in the analysis set is as follows:
；
wherein g=1, 2, 3, 4, … …, k is a positive integer,for the average of the evaluation index PYg in the analysis set, s2 is the standard deviation of the evaluation index PYg in the analysis set.
9. The electrical control cabinet processing quality detection system based on big data detection according to claim 8, wherein if the average value of the evaluation index PYg is smaller than the evaluation index reference threshold and the standard deviation is smaller than the standard deviation reference threshold, generating accidental abnormal information through the comprehensive analysis module, transmitting the accidental abnormal information to the early warning module, and not sending an early warning prompt through the early warning module;
if the average value of the evaluation index PYg is greater than or equal to the evaluation index reference threshold value, the average value of the evaluation index PYg is greater than or equal to the evaluation index reference threshold value and the standard deviation is smaller than the standard deviation reference threshold value, generating abnormal information through the comprehensive analysis module, transmitting the abnormal information to the early warning module, and sending an early warning prompt through the early warning module to prompt the processing personnel that the processing quality of the electrical control cabinet is not in the condition of accidental abnormality.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117071046A (en) * | 2023-10-12 | 2023-11-17 | 山东裕能电力器材有限公司 | Intelligent processing management system for automatic galvanization barrel plating production line |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117071046A (en) * | 2023-10-12 | 2023-11-17 | 山东裕能电力器材有限公司 | Intelligent processing management system for automatic galvanization barrel plating production line |
| CN117071046B (en) * | 2023-10-12 | 2024-01-12 | 山东裕能电力器材有限公司 | Intelligent processing management system for automatic galvanization barrel plating production line |
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